Posted by Lexing Xie and Lydia Lucchesi.

Posted by Lexing Xie and Ziyu Chen.

Thanks to Eric Byler for a 2022 profile article in college news!

We identify Reddit’s Am I the Asshole (AITA) forum as a rich source of data to investigate the moral sphere using AI and machine learning. This work is done with Josh Nguyen and Alasdair Tran, and ANU philosophers Colin Klein, Nick Carroll, and most recently Nick Schuster.

Posted by Alasdair Tran, with edits from Lexing Xie. Thanks to Ingrid Fadelli for sending us interview questions.

In our recent paper published in CVPR 2020, we propose an end-to-end model that can generate linguistically-rich captions for news images. We also build a live demo where people can generate a caption for any New York Times article.

Posted by Minjeong Shin, with edits and additions by Lexing Xie

In our recent paper in IEEE VIS’19, we introduced a new visual metaphor, called the Influence Flowers to represent flow influence between academic entities including people, publication venues, institutions, and topics.

Posted by Lexing Xie. Thanks to Mario Günther, Ignacio Ojea Quintana, Swapnil Mishra and Atoosa Kasirzadeh for many great suggestions.

Fig. 1: Overview of three concurrently evolving timelines in the development of cars². Top (green) - key events in engine and early system developments. Middle (red) - key events in car and road-safety related legislations. Bottom (blue) - rough separation of eras in car styling. Key events selected from Wikipedia narratives on cars and history of the automobile³. See article for discussion. For readability, time scales are not uniform.

posted by Lexing Xie and Alex Mathews

posted by Lexing Xie, additions and edits by Aditya Menon

I like cakes, and I enjoy reading logically lucid research articles. This post argues that research papers can benefit from explicitly thinking about and planning its four logical layers, just like a multi-layered Mille-feuille, or Napoleon cake (image credit: alyonascooking).

posted by Sam Toyer and Sylvie Thiébaux

Planning algorithms try to find series of actions which enable an intelligent agent to achieve some goal.

Such algorithms are used everywhere from manufacturing to robotics to power distribution.

In our recent paper at AAAI ‘18, we showed how to use deep learning techniques from vision and natural language processing to teach a planning system to solve entire classes of sophisticated problems by training on a few simple examples.

posted by Quyu Kong, with edits from Lexing Xie

Sports is a prominent theme in Canberra. This post celebrates two of our competitive athletes, and looks back at two outings when the whole lab flexed our muscles and got a little bit wet.

posted by Lexing Xie

posted by Marian-Andrei Rizoiu , edited by Lexing Xie

“The fundamental scarcity in the modern world is the scarcity of attention.” – Herbert A. Simon.

Human attention is a limited resource and influencing the mechanisms governing its allocation is the holy grail of advertisement. Our ICWSM'17 paper applies the recent HIP popularity modeling to further examine popularity under promotion and answer questions such as:

• How much promotion is required for this item to rise to the top 5% in popularity?
• How fast will an item respond to a given amount of promotion?
• Why is constant promotion desirable?

posted by Marian-Andrei Rizoiu , edited by Lexing Xie

One major gap in understanding social media is to precisely quantify the relationship between the popularity of an online item and the external promotions it receives. Our recent WWW'17 paper supplies the missing link. We use a mathematical model to describe the continuous interaction between external promotions (e.g. tweets about a video) and popularity dynamics (e.g. daily views). This in turn answers several practical questions:

• Can we explain the complex multi-phased popularity dynamics widely seen online?
• Can one predict what could become viral?
• Can one predict what would not become viral even if heavily promoted?